Update app.py

app.py

@@ -157,7 +157,7 @@ def create_hdr_effect(original_image, hdr):
     return Image.fromarray(cv2.cvtColor(hdr_image_8bit, cv2.COLOR_BGR2RGB))
 
 lazy_pipe = LazyLoadPipeline()
-lazy_pipe.load()  # Load the pipeline outside of the GPU function
+lazy_pipe.load()
 
 def prepare_image(input_image, resolution, hdr):
     condition_image = resize_and_upscale(input_image, resolution)
@@ -193,7 +193,14 @@ def gradio_process_image(input_image, resolution, num_inference_steps, strength,
     print("Image processing completed successfully")
     return result
 
-# Gradio interface
+title = """<h1 align="center">Image Upscaler with Tile Controlnet</h1>
+<p align="center">The main ideas come from</p>
+<p><center>
+<a href="https://github.com/philz1337x/clarity-upscaler" target="_blank">[philz1337x]</a>
+<a href="https://github.com/BatouResearch/controlnet-tile-upscale" target="_blank">[Pau-Lozano]</a>
+</center></p>
+"""
+
 with gr.Blocks() as demo:
     gr.Markdown("# Image Enhancement with Stable Diffusion")
     with gr.Row():
@@ -203,9 +210,9 @@ with gr.Blocks() as demo:
         with gr.Column():
             output_image = gr.Image(type="pil", label="Enhanced Image")
     with gr.Accordion("Advanced Options", open=False):
-        resolution = gr.Slider(minimum=512, maximum=2048, value=1024, step=64, label="Resolution")
+        resolution = gr.Slider(minimum=256, maximum=2048, value=512, step=256, label="Resolution")
         num_inference_steps = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Number of Inference Steps")
-        strength = gr.Slider(minimum=0, maximum=1, value=0.35, step=0.05, label="Strength")
+        strength = gr.Slider(minimum=0, maximum=1, value=0.4, step=0.01, label="Strength")
         hdr = gr.Slider(minimum=0, maximum=1, value=0, step=0.1, label="HDR Effect")
         guidance_scale = gr.Slider(minimum=0, maximum=20, value=3, step=0.5, label="Guidance Scale")